Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging

Abstract

This paper proposes a rapid, label-free, and non-invasive approach for identifying murine cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells) using machine-learning-assisted Raman spectroscopic imaging. Through quick Raman spectroscopic imaging, a hyperspectral data processing approach based on machine learning methods proved capable of presenting the cell structure and distinguishing cancer cells from non-cancer muscle cells without compromising full-spectrum information. This study discovered that biomolecular information–nucleic acids, proteins, and lipids—from cells could be retrieved efficiently from low-quality hyperspectral Raman datasets and then employed for cell line differentiation.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

“…95 For example, in a comprehensive approach, He et al applied ten different supervised learning models for discriminating between cancerous and healthy model cell lines. 96 They achieved the highest accuracy of 94.2% with a neural network model, although traditional methods such as QDA and SVM-radial basis function also achieved high classification accuracy of 93.2% and 92.9%, respectively. However, the RS features that determined these classification decisions were not well-defined in neural network-based models, which can limit feature discovery.…”

“…This can be accomplished through PCA by identifying the key principal components and discarding extraneous components that consist of noise, or through supervised ML methods by training an appropriate model. 96 For example, Xu et al trained a deep learning autoencoder (DAE) with pairs of low-acquisition time and high-acquisition time spectrum, which allowed them to remove the noise in rapid scans and accurately classify their data. 100 In another work, He.…”

Recent Advances in Raman Spectral Imaging in Cell Diagnosis and Gene Expression Prediction

“…Because of the high-content data obtained by Raman spectroscopy, it is compatible to other machine learning methods such as random-forest classification, support vector machine or k-means classification and being used in cancer diagnosis or evaluation of cancer treatment [ 33 , 34 , 35 ]. Use of Raman spectra in cancer discrimination is recently becoming popular in combination with machine-learning, including artificial intelligence [ 35 , 36 , 37 ], which is expected to further develop in the near future.…”

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations

“…Their pioneering work follows the recent, rapidly growing use of ML techniques 51 in the development of computational chemistry models. [52][53][54] As far as the solvent effect is concerned, ML models have been developed to capture the effect on chemical reactions, 55,56 spectral properties, [57][58][59][60] identify solvation characteristics in general molecular environment, 50,61,62 and explore the solvent effect on mixture solvent system. [63][64][65] Inspired by the work of Noé and coworkers, 42,49,50,66 in this paper we will also use the solvated alanine dipeptide [67][68][69] as an example and further explore the possibility of "deriving" an implicit solvent model directly from explicit solvent MD simulations.…”

“…Their pioneering work follows the recent, rapidly growing use of ML techniques 51 in the development of computational chemistry models. 52–54 As far as the solvent effect is concerned, ML models have been developed to capture the effect on chemical reactions, 55,56 spectral properties, 57–60 identify solvation characteristics in general molecular environment, 50,61,62 and explore the solvent effect on mixture solvent system. 63–65…”